Import avocado data.
## # A tibble: 10 x 15
## date year month day average_price total_volume small large
## <date> <int> <int> <int> <dbl> <dbl> <dbl> <dbl>
## 1 2015-12-27 2015 12 27 1.33 64237. 1037. 5.45e4
## 2 2015-12-20 2015 12 20 1.35 54877. 674. 4.46e4
## 3 2015-12-13 2015 12 13 0.93 118220. 795. 1.09e5
## 4 2015-12-06 2015 12 6 1.08 78992. 1132 7.20e4
## 5 2015-11-29 2015 11 29 1.28 51040. 941. 4.38e4
## 6 2015-11-22 2015 11 22 1.26 55980. 1184. 4.81e4
## 7 2015-11-15 2015 11 15 0.99 83454. 1369. 7.37e4
## 8 2015-11-08 2015 11 8 0.98 109428. 704. 1.02e5
## 9 2015-11-01 2015 11 1 1.02 99811. 1022. 8.73e4
## 10 2015-10-25 2015 10 25 1.07 74339. 842. 6.48e4
## # … with 7 more variables: extra_large <dbl>, total_bags <dbl>,
## # small_bags <dbl>, large_bags <dbl>, x_large_bags <dbl>, type <chr>,
## # region <chr>
## tibble [18,249 × 15] (S3: tbl_df/tbl/data.frame)
## $ date : Date[1:18249], format: "2015-12-27" "2015-12-20" ...
## $ year : int [1:18249] 2015 2015 2015 2015 2015 2015 2015 2015 2015 2015 ...
## $ month : int [1:18249] 12 12 12 12 11 11 11 11 11 10 ...
## $ day : int [1:18249] 27 20 13 6 29 22 15 8 1 25 ...
## $ average_price: num [1:18249] 1.33 1.35 0.93 1.08 1.28 1.26 0.99 0.98 1.02 1.07 ...
## $ total_volume : num [1:18249] 64237 54877 118220 78992 51040 ...
## $ small : num [1:18249] 1037 674 795 1132 941 ...
## $ large : num [1:18249] 54455 44639 109150 71976 43838 ...
## $ extra_large : num [1:18249] 48.2 58.3 130.5 72.6 75.8 ...
## $ total_bags : num [1:18249] 8697 9506 8145 5811 6184 ...
## $ small_bags : num [1:18249] 8604 9408 8042 5677 5986 ...
## $ large_bags : num [1:18249] 93.2 97.5 103.1 133.8 197.7 ...
## $ x_large_bags : num [1:18249] 0 0 0 0 0 0 0 0 0 0 ...
## $ type : chr [1:18249] "conventional" "conventional" "conventional" "conventional" ...
## $ region : chr [1:18249] "Albany" "Albany" "Albany" "Albany" ...
## [1] 0
Description:
year: 2015-2018
month: 1-12
day: 1-31
type: conventional, organic
fruit_size: small, large, extra_large
bag_type: total, small, large, extra_large
## # A tibble: 10 x 3
## area year gdp
## <chr> <chr> <dbl>
## 1 United States 2015 50301
## 2 United States 2016 50660
## 3 United States 2017 51337
## 4 Alabama 2015 36818
## 5 Alabama 2016 37158
## 6 Alabama 2017 37508
## 7 Alaska 2015 65971
## 8 Alaska 2016 63304
## 9 Alaska 2017 63610
## 10 Arizona 2015 38787
## [1] 0
Description:
year: 2015-2017
wo jue de ke yi zhao you guan xi de che yi che ? https://www.medicalnewstoday.com/articles/270406#benefits https://pdf.usaid.gov/pdf_docs/PA00KP28.pdf
yao bu zhe li zai gao dian data fao.org/faostat/en/#search/Avocados
https://quickstats.nass.usda.gov/results/8A9760E3-BDB0-3A88-B014-DA81BA0845BD
Volume consumption by year: conventional vs. organic 
Time vs. Avocado Consumption by Region
Time vs. Avocado Price by Region
zhe ge tu you dian la ji … yan se ye gai bu liao bu zhi dao wei sha
Avocado Size vs. Volume Sold 
Region vs. Year Average Volume Consumption